In the financial industry, automated teller machines (ATMs) are an important means of providing quality customer service. ATMs provide customers a convenient way to transact bank business while saving time. Accordingly, financial institutions have significant incentive to ensure that their ATMs are continuously available and functioning correctly.
ATM management services are well-known in the art and include, among others, cash replenishment, computerized monitoring system, full first-line maintenance (including replacement of all consumables, screen software uploads and emergency call-outs) and computer video link monitoring. ATM software products are also widely available and include cash management predicting functions. One such product is ATM Manager Pro, available from e-Classic Systems, Inc. This product enables a financial institution to forecast cash out dates and to propose appropriate replenishment amounts, to generate cash orders and loads, to balance vault cash and other cash accounts, and to reconcile settlement details by terminal or by date. In ATM Manager Pro, a model of a withdrawal pattern is generated for a particular machine and then averaged over a given time period (e.g., 28 days) to create a “per day” average for the period. In addition to these known products and services, the patent literature also describes techniques for ATM management such as computer-implemented cash flow predictive algorithms. One such technique is described in U.S. Pat. No. 5,799,288 to Tanaka et al. This patent describes a money management system to predict and manage a demanded cash amount for an ATM. In particular, the system cumulatively stores transaction data from an ATM and uses that data to develop a model of the withdrawal pattern. That model uses data for one (1) year from a number of sources and calculates weights for a number of qualitative factors. A prediction algorithm calculates and predicts demanded cash amounts within a designated time for the machine based on the model.
These and other known approaches provide useful information to the financial institution or other ATM service provider, but their cash demand forecasts are relatively simple and do not take into consideration true machine use situations or the particular ATM's operating environment. Thus, for example, a financial institution might expect a withdrawal pattern on Fridays to be somewhat similar over a given month but perhaps very different from the withdrawal pattern on Wednesdays, as Fridays are often pay days or days in which users withdraw money for weekends. Depending upon the location of the ATM, withdrawals also may be affected by season, e.g., ATMs located in ski areas experience higher demand in winter and lower demand in the summer; conversely, ATMs located in towns near beaches experience higher demand in summer and lower demand in winter. An institution may also experience unexpected demand from a given machine in close proximity to a venue at which a given concert or special event is taking place. Prior art cash demand forecasting techniques are often based upon rules of thumb, relying upon experienced operators, and they are not robust enough to provide meaningful forecasts for these and other “real-world” situations. As a result, ATMs are often loaded with extra funds, reducing the profits of the ATM operator, or they are loaded with not enough funds, resulting into unsatisfied customers and lost revenue for the ATM operator.
The present invention addresses the long felt need in the art for improved techniques for forecasting cash demand and load schedules for automated teller machines.